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Presentation on the Moon to Mars Planetary Autonomous Construction Technology (MMPACT) which provides basic project information and emphasizes interdependencies and technology gaps.
This document represents a catalogue of site information for the Crustal Dynamics Project. It contains information and descriptions of those sites used by the Project as observing stations for making the precise geodetic measurements useful for studies of the Earth's crustal movements and deformation.
This document represents a catalog of site information for the Crustal Dynamics Project. It contains information on and descriptions of those sites used by the Project as observing stations for making the precise geodetic measurements necessary for studies of the Earth's crustal movements and deformation.
ABSTRACT The power spectrum of the non-linearly evolved large-scale mass distribution recovers only a minority of the information available on the mass fluctuation amplitude. We investigate the recovery of this information in 2D ‘slabs’ of the mass distribution averaged over ≈100 h−1 Mpc along the line of sight, as might be obtained from photometric redshift surveys. We demonstrate a Hamiltonian Monte Carlo method to reconstruct the non-Gaussian mass distribution in slabs, under the assumption that the projected field is a point-transformed Gaussian random field, Poisson-sampled by galaxies. When applied to the Quijote N-body suite at z = 0.5 and at a transverse resolution of 2 h−1 Mpc, the method recovers ∼30 times more information than the 2D power spectrum in the well-sampled limit, recovering the Gaussian limit on information. At a more realistic galaxy sampling density of 0.01 h3 Mpc−3, shot noise reduces the information gain to a factor of 5 improvement over the power spectrum at resolutions of 4 h−1 Mpc or smaller.
Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).
The product of research projects is information. Through the life cycle of a project, information comes from many sources and takes many forms. Traditionally, this body of information is summarized in a formal publication, typically a journal article. While formal publications enjoy the benefits of peer review and technical editing, they are also often compromises in media format and length. As such, we consider a formal publication to represent an abstract to a larger body of work: a pyramid of scientific and technical information (STI). While this abstract may be sufficient for some applications, an in-depth use or analysis is likely to require the supporting layers from the pyramid. We have developed buckets to preserve this pyramid of STI. Buckets provide an archive- and protocol-independent container construct in which all related information objects can be logically grouped together, archived, and manipulated as a single object. Furthermore, buckets are active archival objects and can communicate with each other, people, or arbitrary network services. Buckets are an implementation of the Smart Object, Dumb Archive (SODA) DL model. In SODA, data objects are more important than the archives that hold them. Much of the functionality traditionally associated with archives is pushed down into the objects, such as enforcing terms and conditions, negotiating display, and content maintenance. In this paper, we discuss the motivation, design, and implication of bucket use in DLs with respect to grey literature.
Federal attempts to stimulate technological innovation have been unsuccessful because of the application of an inappropriate policy framework that lacks conceptual and empirical knowledge of the process of technological innovation and fails to acknowledge the relationship between knowled reproduction, transfer, and use as equally important components of the process of knowledge diffusion. It is argued that the potential contributions of high-speed computing and networking systems will be diminished unless empirically derived knowledge about the information-seeking behavior of the members of the social system is incorporated into a new policy framework. Findings from the NASA/DoD Aerospace Knowledge Diffusion Research Project are presented in support of this assertion.
Accurate estimates can be created in less than a minute by applying powerful techniques and algorithms to create an Excel-based parametric cost model. In five easy steps you will learn how to normalize your company 's historical cost data to the new project parameters. This paper provides a complete, easy-to-understand, step by step how-to guide. Such a guide does not seem to currently exist. Over 2,000 hours of research, data collection, and trial and error, and thousands of lines of Excel Visual Basic Application (VBA) code were invested in developing these methods. While VBA is not required to use this information, it increases the power and aesthetics of the model. Implementing all of the steps described, while not required, will increase the accuracy of the results.
Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.
The CO2-Locate Database is a growing compilation of publicly available wellbore resources that have been merged based on common attributes across data sources with an attribute schema developed to be consistent across disparate resources, reduce data gaps, and eliminate record redundancy. The first version of CO2-Locate has been published to Energy Data eXchange (EDX) and includes the integrated public wells dataset as well as additional geospatial summary layers of key wellbore characteristics to protect proprietary resources. Additionally, the CO2-Locate database has been deployed into a web application, enabling easy access, data filtering capabilities, and visualization of U.S. wellbore infrastructure by stakeholders to inform injection site selection and risk assessments.
From the NASA/DOD survey data, there can be no way of inferring what strategy for knowledge transfer is best; indeed, given the fact that the respondents were all presumably well qualified professionals, the data tend to call into serious question the idea that any one model might meet the needs of more than a distinct minority of possible users. The evidence to date appears to reinforce the concept that different information environments take many different shapes, and interact with each other and with formal data transmission sources in many different and equally valuable ways. Any overall strategy for improving the effectiveness and efficiency of scientific and technical information sharing must take this divergence into account, and work toward the creation of systems that reinforce true interactive knowledge utilization rather than simply disseminating data.
The ability of large information systems to support the changing data requirements of active science projects is being tested in a NASA collaborative study. This paper briefly profiles both the active science project and the large information system involved in this effort and offers some observations about the effectiveness of the project support. This is followed by lessons that are important for those participating in large information systems that need to support active science projects or that make available the valuable data produced by these projects. We learned in this work that it is difficult for a large information system focused on long term data management to satisfy the requirements of an on-going science project. For example, in order to provide the best service, it is important for all information system staff to keep focused on the needs and constraints of the scientists in the development of appropriate services. If the lessons learned in this and other science support experiences are not applied by those involved with large information systems of the EOS (Earth Observing System) era, then the final data products produced by future science projects may not be robust or of high quality, thereby making the conduct of the project science less efficacious and reducing the value of these unique suites of data for future research.
Information is given in viewgraph form on the activities of the Flight Projects Division of NASA's Office of Aeronautics and Space Technology. Information is given on space research and technology strategy, current space flight experiments, the Long Duration Exposure Facility, the Orbiter Experiment Program, the Lidar In-Space Technology Experiment, the Ion Auxiliary Propulsion System, the Arcjet Flight Experiment, the Telerobotic Intelligent Interface Flight Experiment, the Cryogenic Fluid Management Flight Experiment, the Industry/University In-Space Flight Experiments, and the Aeroassist Flight Experiment.
This presentation describes a distributed, data-centric collaborative information management (DDCIM) approach for project management and SE&I that was successfully implemented on two in-house NASA flight test projects.
Public streetlight charging, whether on streets in central business districts or residential areas, provides easy charging access for apartment residents and homeowners alike. While most electric vehicle (EV) drivers charge at home, they do so in garages or on driveways they own. For renters and residents of multifamily housing (MFH), however, this may not be an option. EVs have a lower cost of ownership compared to conventional vehicles, and a used EV may be an affordable option for a lower-income household. But without easy access to charging, even a low-cost used EV may not be an option for a prospective buyer. An affordable curbside charging network has the potential to expand EV adoption into neighborhoods that have to date seen minimal interest and uptake of the technology and associated charging infrastructure. Streetlight charging networks can provide an economical, scalable, and effective approach to providing equitable and convenient charging. Metropolitan Energy Center (MEC) is dedicated to the mission of creating resource efficiency, environmental health, and economic vitality in the Kansas City region and beyond. Since 1983, MEC has provided resources, outreach, and training to make alternative fuels and energy efficiency commonplace. MEC led a streetlight charging pilot project that installed limited EV charging infrastructure on the streetlight system in Kansas City, Missouri, to demonstrate and test the benefits of curbside charging for EVs at existing on-street parking locations. The project aimed to cost-effectively expand the charging network in Kansas City to support residential charging and provide infrastructure in one or more charging deserts throughout the city. This pilot evaluates the impact and overall success of streetlight charging based on community feedback, utilization of charging infrastructure, technical feasibility, and cost. The project has pursued a data- and community-driven site selection process designed to identify sites with high demand and high opportunity for EV charging. This project was funded by the U.S. Department of Energy (DOE) and awarded to MEC through a competitive proposal process. The novelty and complexity of this project required an organization that could facilitate collaboration across levels of government, community members, and industry partners. For the past 25 years, through Kansas City Regional Clean Cities, MEC has worked with numerous public and private fleets on a variety of projects to improve the environmental performance and efficiency of the regional vehicle fleet. To advance affordable, efficient, and clean transportation efforts, DOE Clean Cities and Communities coalitions create local networks of public and private sector stakeholders and engage communities. Rooted within their local communities, the coalitions serve as experts and ambassadors, bringing to bear the collective knowledge, experience, and practical know-how of the entire network from within DOE, its national laboratories, and diverse stakeholders in the field. MEC and its project partners made in-kind contributions to leverage federal dollars for the benefit of the Kansas City community. Findings from this project will help determine the best applications for streetlight charging technologies to maximize funding impact and serve community needs. The team evaluated locations based on expected charging demand, technical feasibility, safety considerations, and enhanced charging network siting needs. Throughout the project, the team gathered feedback and evaluated ways to make public charging for EVs available to all community members. The insights will help Kansas City and other communities streamline future efforts to support EV drivers through public charging in the city right-of-way. Furthermore, this project will inform citywide guidance for future installations. MEC is committed to a transparent and publicly accessible approach that encourages the collaborative evaluation of streetlight charging. The project has engaged the community to proactively identify and evaluate the benefits and impacts of streetlight charging. It was a priority for the project to ensure the benefits of this pilot are distributed equitably to all members of the Kansas City community and that new charging opportunities and associated resources are available in diverse neighborhoods across the city. The charging infrastructure supports an affordable curbside charging network that will enable more drivers to choose EVs and provide easy charging access for all community members interested in driving an EV. The community feedback received through this project informed future resources and opportunities to make EVs more accessible to all members of the Kansas City community. MEC worked with several community partners on this project, including Missouri University of Science and Technology (MST), Pennsylvania State University (Penn State), the National Renewable Energy Laboratory (NREL); the city of Kansas City, Missouri; Evergy; Black and McDonald (B&M); LilyPad EV; EVNoire; and Westside Housing Organization (WHO). Project partners contributed to the cost match required for DOE grants through capital expenditures, personnel, and other in-kind contributions. Detailed descriptions of project team organizations can be found in Appendix A. Project Partners. Analysts at NREL and MST/PennState developed site maps based on demand and equity considerations. MEC conducted outreach to community members to garner input on project design and site selection, and received approval from the Missouri Public Service Commission (PSC) for Evergy’s EV charging station ownership. MEC worked with all partners to gather additional siting criteria and developed a site selection evaluation checklist, and partners conducted site visits to proposed installation sites. Next, B&M, Evergy, and the city executed all site agreements, conducted site-specific engineering design, acquired associated permits, and issued notices to proceed site by site or in small batches. Finally, from January to April 2023, the project team installed 23 EV charging stations built on Kansas City’s streetlight system in six council districts. Evergy will own, operate, and monitor the stations for 10 years, sharing charging data with MEC for at least 1 year.
Information is given in viewgraph form on the Artemis Common Lunar Lander project status. The plans are to start the Space Exploration Initiative (SEI) with lunar robotic missions that can demonstrate the NASA cultural change and provide a catalyst for human exploration of the moon and Mars. The Artemis Common Lunar Lander Concept developed by the Johnson Space Center (JSC) has been accepted as the centerpiece of this lunar robotic exploration program. Topics covered include the anticipated program structure, a concept overview, lander value as a function of payload mass, the approach of the JSC in-house study, an example launch vehicle packaging concept, and the use of the Delta 2 launch vehicle.
Informational briefing on NASA activities related to the UAS integration in the NAS project.
Information is given in viewgraph form on the Space Station Freedom (SSF) Thermal Control System Automation Project (TCSAP). Topics covered include the assembly of the External Thermal Control System (ETCS); the ETCS functional schematic; the baseline Fault Detection, Isolation, and Recovery (FDIR), including the development of a knowledge based system (KBS) for application of rule based reasoning to the SSF ETCS; TCSAP software architecture; the High Fidelity Simulator architecture; the TCSAP Runtime Object Database (RODB) data flow; KBS functional architecture and logic flow; TCSAP growth and evolution; and TCSAP relationships.